code: def mine(self, embeddings, labels, ref_emb, ref_labels):
      mat = self.distance(embeddings, ref_emb)
      a1, p, a2, n = lmu.get_all_pairs_indices(labels, ref_labels)
      pos_pairs = mat[a1, p]
      neg_pairs = mat[a2, n]
  
      if len(pos_pairs) > 0:
          a1, p = self.get_uniformly_distributed_pairs(
              pos_pairs, a1, p, self.pos_per_bin
          )
  
      if len(neg_pairs) > 0:
          a2, n = self.get_uniformly_distributed_pairs(
              neg_pairs, a2, n, self.neg_per_bin
          )
  
      return a1, p, a2, n
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/miners/uniform_histogram_miner.py
imports: import torch
  from ..utils import loss_and_miner_utils
  from .base_miner import BaseMiner
label_desc: Use image node_id 2 for calling the UniformHistogramMiner obj's underlying member method code with example usage: obj.mine(embeddings, labels, ref_emb, ref_labels) and returns: a1,  p,  a2,  n